[1] 罗浩, 姜伟, 范星, 等. 基于深度学习的行人重识别研究进展[J]. 自动化学报, 2019, 45(11): 2032-2049.
LUO H, JIANG W, FAN X, et al. A survey on deep learning based person re-identification[J]. Acta Automatica Sinica, 2019, 45(11): 2032-2049.
[2] ZHANG X, LUO H, FAN X, et al. Alignedreid: surpassing human-level performance in person re-identification[J]. arXiv:1711.08184, 2017.
[3] MIAO J, WU Y, LIU P, et al. Pose-guided feature alignment for occluded person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 542-551.
[4] GAO S, WANG J, LU H, et al. Pose-guided visible part matching for occluded person ReID[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11744-11752.
[5] 许茹玉, 吴琳, 粟兴旺, 等. 多样细粒度特征与关系网络驱动的行人重识别[J]. 计算机工程与应用, 2023, 59(19): 211-219.
XU R Y, WU L, SU X W, et al. Person re-identification driven by diverse fine-grained features and relation network[J]. Computer Engineering and Applications, 2023, 59(19): 211-219.
[6] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[J]. arXiv:2010.11929, 2020.
[7] HE S, LUO H, WANG P, et al. TransReID: transformer-based object re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 15013-15022.
[8] TAN L, DAI P, JI R, et al. Dynamic prototype mask for occluded person re-identification[C]//Proceedings of the 30th ACM International Conference on Multimedia, 2022: 531-540.
[9] LUO H, GU Y, LIAO X, et al. Bag of tricks and a strong baseline for deep person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019: 1487-1495.
[10] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.
[11] HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification[J]. arXiv:1703.07737, 2017.
[12] DENG J, GUO J, XUE N, et al. ArcFace: additive angular margin loss for deep face recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 4690-4699.
[13] ZHUO J, CHEN Z, LAI J, et al. Occluded person re-identification[C]//Proceedings of the IEEE International Conference on Multimedia and EXPO, 2018: 1-6.
[14] ZHENG W S, LI X, XIANG T, et al. Partial person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 4678-4686.
[15] ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification: a benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1116-1124.
[16] ZHAO L, LI X, ZHUANG Y, et al. Deeply-learned part-aligned representations for person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 3219-3228.
[17] SUN Y, ZHENG L, YANG Y, et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the European Conference on Computer Vision, 2018: 480-496.
[18] HE L, LIANG J, LI H, et al. Deep spatial feature reconstruction for partial person re-identification: alignment-free approach[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7073-7082.
[19] ZHU K, GUO H, LIU Z, et al. Identity-guided human semantic parsing for person re-identification[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2020: 346-363.
[20] JIA M, CHENG X, ZHAI Y, et al. Matching on sets: conquer occluded person re-identification without alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(2): 1673-1681.
[21] SUH Y, WANG J, TANG S, et al. Part-aligned bilinear representations for person re-identification[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 402-419.
[22] WANG G, YANG S, LIU H, et al. High-order information matters: learning relation and topology for occluded person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 6449-6458.
[23] LI Y, HE J, ZHANG T, et al. Diverse part discovery: occluded person re-identification with part-aware transformer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 2898-2907.
[24] WANG T, LIU H, SONG P, et al. Pose-guided feature disentangling for occluded person re-identification based on transformer[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(3): 2540-2549.
[25] HE L, SUN Z, ZHU Y, et al. Recognizing partial biometric patterns[J]. arXiv:1810.07399, 2018.
[26] SUN H, CHEN Z, YAN S, et al. MVP matching: a maximum-value perfect matching for mining hard samples, with application to person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 6737-6747.
[27] LUO C, CHEN Y, WANG N, et al. Spectral feature transformation for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 4976-4985.
[28] KALAYEH M M, BASARAN E, G?KMEN M, et al. Human semantic parsing for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 1062-1071.
[29] LIU J, NI B, YAN Y, et al. Pose transferrable person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4099-4108.
[30] MA Z, ZHAO Y, LI J. Pose-guided inter-and intra-part relational transformer for occluded person re-identification[C]//Proceedings of the 29th ACM International Conference on Multimedia, 2021: 1487-1496. |